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Sentiment Analysis Applied to IBOVESPA Prediction

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Optimization, Learning Algorithms and Applications (OL2A 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1754))

Abstract

Social media is increasingly being used as a source of news, a trend which has resulted in large amounts of data. This work presents an evaluation strategy for assessing the impact that social media has on the Bovespa Index (IBovespa), the benchmark index of the Brazilian stock market. A total of 105000 tweets were collected from the twitter profile of “G1 Economia”, one of the main Brazilian finance portals. This data was processed using sentiment analysis methods which were then incorporated into the development of an artificial neural network whose objective was to predict the IBovespa. A hyperparameter optimization study is also presented. The experimental results show that of the 1279 topologies studied, \(82.4\%\) exhibited better performance when using sentiment analysis in conjunction with historical data, against the baseline of using only the latter. Curiously, even though the average performance was higher, the absolute best result was obtained without the use of NLP techniques. In the context of the method developed and the data used, it appears that approaches using sentiment analysis alongside historical records may be more effective than using only one or the other.

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Notes

  1. 1.

    https://gitlab.com/yngwi/SA_IBOVESPA_Prediction.

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Acknowledgements

This work has been financially supported by national grant from the FCT (Fundação para a Ciência e a Tecnologia), under the project UIDB/05567/2020.

Author information

Authors and Affiliations

  1. Celso Suckow da Fonseca Federal Center of Technological Education, Rio de Janeiro, RJ, Brazil

    Yngwi Guimarães Vieira Souza, Luís Tarrataca & Laura Silva de Assis

  2. Smart Cities Research Center, Polytechnic Institute of Tomar, Tomar, Portugal

    Douglas O. Cardoso

Authors
  1. Yngwi Guimarães Vieira Souza
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  2. Luís Tarrataca
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  3. Douglas O. Cardoso
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  4. Laura Silva de Assis
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Corresponding author

Correspondence to Luís Tarrataca .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. University of Ljubljana, Ljubljana, Slovenia

    Andrej Košir

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

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Souza, Y.G.V., Tarrataca, L., Cardoso, D.O., Assis, L.S.d. (2022). Sentiment Analysis Applied to IBOVESPA Prediction. In: Pereira, A.I., Košir, A., Fernandes, F.P., Pacheco, M.F., Teixeira, J.P., Lopes, R.P. (eds) Optimization, Learning Algorithms and Applications. OL2A 2022. Communications in Computer and Information Science, vol 1754. Springer, Cham. https://doi.org/10.1007/978-3-031-23236-7_26

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  • DOI: https://doi.org/10.1007/978-3-031-23236-7_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23235-0

  • Online ISBN: 978-3-031-23236-7

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